Construction of 3D human distal femoral surface models using a 3D statistical deformable model

被引:54
|
作者
Zhu, Zhonglin [1 ,2 ]
Li, Guoan [1 ]
机构
[1] Harvard Univ, Massachusetts Gen Hosp, Sch Med, Bioengn Lab,Dept Orthopaed Surg, Boston, MA 02114 USA
[2] Tsinghua Univ, Dept Biomed Engn, Beijing 100084, Peoples R China
基金
美国国家卫生研究院;
关键词
Statistical shape model; Knee; 3D knee model; Fluoroscopic images; PROXIMAL FEMUR; SHAPE MODELS; RECONSTRUCTION; VALIDATION; REGISTRATION; RADIOGRAPHS; KINEMATICS; SYSTEM;
D O I
10.1016/j.jbiomech.2011.07.006
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
Construction of 3D geometric surface models of human knee joint is always a challenge in biomedical engineering. This study introduced an improved statistical shape model (SSM) method that only uses 2D images of a joint to predict the 3D joint surface model. The SSM was constructed using 40 distal femur models of human knees. In this paper, a series validation and parametric analysis suggested that more than 25 distal femur models are needed to construct the SSM; each distal femur should be described using at least 3000 nodes in space; and two 2D fluoroscopic images taken in 45 directions should be used for the 3D surface shape prediction. Using this SSM method, ten independent distal femurs from 10 independent living subjects were predicted using their 2D plane fluoroscopic images. The predicted models were compared to their native 3D distal femur models constructed using their 3D MR images. The results demonstrated that using two fluoroscopic images of the knee, the overall difference between the predicted distal femur surface and the MR image-based surface was 0.16 +/- 1.16 mm. These data indicated that the SSM method could be a powerful method for construction of 3D surface geometries of the distal femur. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2362 / 2368
页数:7
相关论文
共 50 条
  • [1] Automated 3D PDM construction using deformable models
    Kaus, MR
    Pekar, V
    Lorenz, C
    Truyen, R
    Lobregt, S
    Richolt, J
    Weese, J
    EIGHTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOL I, PROCEEDINGS, 2001, : 566 - 572
  • [2] 3D surface reconstruction using fuzzy deformable models
    Xia, LM
    Gu, SW
    Shen, XQ
    Fei, YP
    2000 5TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS I-III, 2000, : 886 - 889
  • [3] Construction of 3D dynamic statistical deformable models for complex topological shapes
    Horkaew, P
    Yang, GZ
    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2004, PT 1, PROCEEDINGS, 2004, 3216 : 217 - 224
  • [4] Monocular Surface Reconstruction Using 3D Deformable Part Models
    Kinauer, Stefan
    Berman, Maxim
    Kokkinos, Iasonas
    COMPUTER VISION - ECCV 2016 WORKSHOPS, PT III, 2016, 9915 : 296 - 308
  • [5] Using surface variability characteristics for segmentation of deformable 3D objects with application to piecewise statistical deformable model
    Peng Du
    Horace H. S. Ip
    Bei Hua
    Jun Feng
    The Visual Computer, 2012, 28 : 493 - 509
  • [6] Using surface variability characteristics for segmentation of deformable 3D objects with application to piecewise statistical deformable model
    Du, Peng
    Ip, Horace H. S.
    Hua, Bei
    Feng, Jun
    VISUAL COMPUTER, 2012, 28 (05): : 493 - 509
  • [7] A new scheme for automated 3D PDM construction using deformable models
    Zhao, ZheEn
    Teoh, Eam Khwang
    IMAGE AND VISION COMPUTING, 2008, 26 (02) : 275 - 288
  • [8] A novel framework for automated 3D PDM construction using deformable models
    Zhao, Z
    Teoh, EK
    MEDICAL IMAGING 2005: IMAGE PROCESSING, PT 1-3, 2005, 5747 : 303 - 314
  • [9] Tomographic reconstruction using 3D deformable models
    Battle, XL
    Cunningham, GS
    Hanson, KM
    PHYSICS IN MEDICINE AND BIOLOGY, 1998, 43 (04): : 983 - 990